Extracting Tree Species Distribution with Landsat 8 OLI Data

doi:10.11849/zrzyxb.20160730

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JOURNAL OF NATURAL RESOURCES ›› 2017, Vol. 32 ›› Issue (7) : 1193-1203. DOI: 10.11849/zrzyxb.20160730

Resource Evaluation

Extracting Tree Species Distribution with Landsat 8 OLI Data

CHI Yu-feng^a, LAI Ri-wen^a, YU Li-li^a, ZHANG Ze-jun^b, SU Yan-qin^a, YING Xing-liang^a

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Abstract

Investigation the distribution of the tree species is significant in the forestry work, and extracting the tree species information from remote sensing images plays an important role. Changting County is located in southwest part of Fujian Province, China. The topography in Changting is characterized by mountains and hills. The climate is humid. The mean temperature is 18 ℃, and the annual precipitation is 1 742.8 mm. Forests cover large proportion of the area in Changting County, more than 80%. In this study, multispectral Landsat-8 OLI imagery data obtained on 22 March 2016 were used. Normalized difference vegetation index (NDVI) was used to distinguish the distribution of the vegetations. From December 2015 to February 2016, 550 sample points were detected which contained four different tree species. One hundred points were used to build knowledge based system (KBS), and the rest 450 points were taken to verify the accuracy of the classification. For the KBS, different tree species have different means of spectral threshold values. Half standard deviation of the threshold value of the 100 points was taken to build the initial KBS, and then it was modified by the good result of the classification. Image expansion method was used to classify the forests. The results of the classification were validated with ground verification data, and compared with results derived from expert knowledge classification. The results show that image expansion method combining spectral and spatial characteristics of different tree species improve the classification accuracy. The overall accuracy and Kappa coefficient were 83.01% and 0.77, respectively, increased by 8.25% and 0.11 when compared with expert knowledge classification. The presented study could provide a reference for distinguishing tree species for forestry investigation with Landsat 8 OLI data.

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image expansion / knowledge system / tree species

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CHI Yu-feng, LAI Ri-wen, YU Li-li, ZHANG Ze-jun, SU Yan-qin, YING Xing-liang. Extracting Tree Species Distribution with Landsat 8 OLI Data[J]. JOURNAL OF NATURAL RESOURCES, 2017, 32(7): 1193-1203 https://doi.org/10.11849/zrzyxb.20160730

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